Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis

OBJECTIVES: To test the hypothesis that the spatial distribution of residential burglary is shaped by the configuration of the street network, as predicted by, for example, crime pattern theory. In particular, the study examines whether burglary risk is higher on street segments with higher usage potential. METHODS: Residential burglary data for Birmingham (UK) are examined at the street segment level using a hierarchical linear model. Estimates of the usage of street segments are derived from the graph theoretical metric of betweenness, which measures how frequently segments feature in the shortest paths (those most likely to be used) through the network. Several variants of betweenness are considered. The geometry of street segments is also incorporated—via a measure of their linearity—as are several socio-demographic factors. RESULTS: As anticipated by theory, the measure of betweenness was found to be a highly-significant predictor of the burglary victimization count at the street segment level for all but one of the variants considered. The non-significant result was found for the most localized measure of betweenness considered. More linear streets were generally found to be at lower risk of victimization. CONCLUSIONS: Betweenness offers a more granular and objective means of measuring the street network than categorical classifications previously used, and its meaning links more directly to theory. The results provide support for crime pattern theory, suggesting a higher risk of burglary for streets with more potential usage. The apparent negative effect of linearity suggests the need for further research into the visual component of target choice, and the role of guardianship

City flight migration patterns in the East Midlands

This paper investigates migration to and from the Three Cities of Derby, Leicester and Nottingham. It investigates whether these cities lose more migrants than they gain from more rural parts of the city-regions and the extent to which these flows impact on the demographic profiles of the Three Cities

Hot Routes: Developing a New Technique for the Spatial Analysis of Crime

The use of hotspot mapping techniques such as KDE to represent the geographical spread of linear events can be problematic. Network-constrained data (for example transport-related crime) require a different approach to visualize concentration. We propose a methodology called Hot Routes, which measures the risk distribution of crime along a linear network by calculating the rate of crimes per section of road. This method has been designed for everyday crime analysts, and requires only a Geographical Information System (GIS), and suitable data to calculate. A demonstration is provided using crime data collected from London bus routes

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks